Phoneme Stress Count Phoneme Stress Count
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Phoneme-free prosodic representations are involved in pre-lexical and lexical neurobiological mechanisms underlying spoken word processing
Recently we reported that spoken stressed and unstressed primes differently modulate Event Related Potentials (ERPs) of spoken initially stressed targets. ERP stress priming was independent of prime-target phoneme overlap. Here we test whether phoneme-free ERP stress priming involves the lexicon. We used German target words with the same onset phonemes but different onset stress, such as MANdel...
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Speech is characterized by phonemes and prosody. Neurocognitive evidence supports the separate processing of each type of information. Therefore, one might suggest individual development of both pathways. In this study, we examine literacy acquisition in middle childhood. Children become aware of the phonemes in speech at that time and refine phoneme processing when they acquire an alphabetic w...
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This paper applies the Competitive Neural Tree (CNeT) method to phoneme recognition, a pattern classiication problem. CNeTs combine the advantages of Decision Trees and Competitive Neural Networks. The CNeT algorithm works by hierarchically clustering given examples while growing a tree. Diierent search methods, as well as stopping and splitting criteria are discussed. The CNeT algorithm allows...
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Improving phoneme recognition has attracted the attention of many researchers due to its applications in various fields of speech processing. Recent research achievements show that using deep neural network (DNN) in speech recognition systems significantly improves the performance of these systems. There are two phases in DNN-based phoneme recognition systems including training and testing. Mos...
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Word pronunciations, consisting of phoneme sequences and the associated syllabification and stress patterns, are vital for both speech recognition and text-to-speech (TTS) systems. For speech recognition phoneme sequences for words may be learned from audio data. We train recurrent neural network (RNN) based models to predict the syllabification and stress pattern for such pronunciations making...
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